DocumentCode :
3752240
Title :
Calibration of word posterior estimation in confusion networks for keyword search
Author :
Zhiqiang Lv;Meng Cai;Wei-Qiang Zhang;Jia Liu
Author_Institution :
Tsinghua University, Beijing, China
fYear :
2015
Firstpage :
148
Lastpage :
151
Abstract :
Word posterior probability has been widely used as the confidence estimation of automatic speech recognition (ASR) systems and has been proved to be quite effective in related applications such as keyword search. However, word posterior probability tends to overestimate the true probability of a hypothesis, as it is computed on a subset of the total hypothesis space. In this paper, we show that a more accurate estimation of posterior can be obtained by using a calibration method based on the conditional random field (CRF) model. By using calibrated posterior estimation for keyword search task, we obtain a maximum absolute gain of 1.15% for single-word keyword search on the maximum term-weighted value (MTWV) metric on the OpenKWS14 Tamil dataset.
Keywords :
"Estimation","Feature extraction","Keyword search","Support vector machines","Lattices","Speech","Calibration"
Publisher :
ieee
Conference_Titel :
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015 Asia-Pacific
Type :
conf
DOI :
10.1109/APSIPA.2015.7415491
Filename :
7415491
Link To Document :
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